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    Kallak/Gállok Volvo autonomous fleet: mine design and winter haulage notes for engineers

    July 3, 2026|

    Reviewed by Tom Sullivan

    First reported on International Mining – News

    30 Second Briefing

    Volvo Autonomous Solutions has signed a Letter of Intent with Jokkmokk Iron Mines AB to develop an autonomous haulage fleet for the planned Kallak/Gállok iron ore project north of the Arctic Circle, targeting operation in sub-zero, snow and ice conditions. The partners will test downsized, purpose-built autonomous vehicles rather than conventional 220–300 t trucks, aiming to optimise haul profiles, ramp geometry and energy use for a relatively compact open pit. For engineers, the project offers an early Arctic testbed for integrating autonomy with mine design, traffic management and winter road maintenance strategies.

    Technical Brief

    • LOI structure allows staged validation of Volvo’s autonomous haulage system before full commercial deployment.
    • Volvo Autonomous Solutions will supply both vehicles and site-wide traffic management and control infrastructure.
    • Collaboration scope includes adapting autonomous haulage to local regulatory requirements for public-road interfaces.
    • Data from early operations will be used to refine vehicle control algorithms for low-adhesion surfaces.
    • Project provides a reference case for integrating OEM-run autonomous haulage as a service in greenfield mines.

    Our Take

    Volvo Autonomous Solutions’ prior deployments at Brønnøy Kalk’s Velfjord limestone mine and Boliden’s Garpenberg tailings project suggest that Jokkmokk Iron’s Kallak/Gállok operation north of the Arctic Circle is tapping into a maturing, not experimental, autonomous haulage platform for harsh Nordic conditions.

    With series assembly of Volvo FH Autonomous trucks already underway at Säffle, Jokkmokk Iron Mines AB can likely access standardised hardware and support, reducing lead times and integration risk compared with bespoke autonomous fleets at other greenfield projects in our mining database.

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    Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.

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